A Study of ROC Curve for Two-Stage Calibration Method from Primary Large and External Small Databases: An Example of Cardiovascular Event with Type 2 Diabetes Mellitus

Abstract

The confounding bias problem from missing or unmeasured confounders is one of the biggest challenge in observational studies Lin and Chen (2014) developed a 2-stage calibration (TSC) method to obtain the estimate of treatment effect which summarizes the confounding information from the large-scale administrative database and the small-scale survey database The small-scale dataset contains important confounding information in addition to the confounding variables in the large-scale dataset However up to our knowledge this approach does not provide diagnostic accuracy This paper is trying to extend TSC method for binary outcome to build new TSC logistic regression model and use the area under the receive operating characteristic curve (AUC) to assess the diagnostic accuracy In application we assess the relationship between composite cardiovascular disease (CVD) and two hypoglycemic agents adjusting for the confounders such as complication and comorbidity The important confounder- glycated hemoglobin (HbA1c) is only obtained from the external small dataset The proposed method is demonstrated based on the type 2 diabetes mellitus patients in Taiwan National Health Insurance Research Database and National Cheng Kung University Hospital

Date of Award	2020
Original language	English
Supervisor	Pei-Fang Su (Supervisor)